Ensemble learning is a term from artificial intelligence as well as the fields of big data and smart data. It involves several different „models“ – which are programmes that learn from data – working together to achieve better results than a single model alone.
Imagine ensemble learning as a team of experts making a decision together. Each expert offers their own opinion, and collectively, they often arrive at a better solution than any single expert could alone. In artificial intelligence, this principle is used for things like making predictions about customer behaviour or automatically recognising text.
A simple example: Suppose you want to use Artificial Intelligence to predict whether a customer will buy a product. A single model could be wrong. However, if you use multiple models that „think“ differently, they can vote. The majority decides, and the prediction becomes more reliable.
Ensemble learning therefore ensures that computer systems work more precisely and make fewer errors. This method is particularly useful in areas where a lot of data needs to be analysed.
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